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Model-based prediction of human hair color using DNA variants

Overview of attention for article published in Human Genetics, January 2011
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#46 of 3,060)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

news
2 news outlets
blogs
4 blogs
twitter
5 X users
patent
1 patent
facebook
1 Facebook page
wikipedia
7 Wikipedia pages
video
1 YouTube creator

Citations

dimensions_citation
156 Dimensions

Readers on

mendeley
284 Mendeley
citeulike
7 CiteULike
Title
Model-based prediction of human hair color using DNA variants
Published in
Human Genetics, January 2011
DOI 10.1007/s00439-010-0939-8
Pubmed ID
Authors

Wojciech Branicki, Fan Liu, Kate van Duijn, Jolanta Draus-Barini, Ewelina Pośpiech, Susan Walsh, Tomasz Kupiec, Anna Wojas-Pelc, Manfred Kayser

Abstract

Predicting complex human phenotypes from genotypes is the central concept of widely advocated personalized medicine, but so far has rarely led to high accuracies limiting practical applications. One notable exception, although less relevant for medical but important for forensic purposes, is human eye color, for which it has been recently demonstrated that highly accurate prediction is feasible from a small number of DNA variants. Here, we demonstrate that human hair color is predictable from DNA variants with similarly high accuracies. We analyzed in Polish Europeans with single-observer hair color grading 45 single nucleotide polymorphisms (SNPs) from 12 genes previously associated with human hair color variation. We found that a model based on a subset of 13 single or compound genetic markers from 11 genes predicted red hair color with over 0.9, black hair color with almost 0.9, as well as blond, and brown hair color with over 0.8 prevalence-adjusted accuracy expressed by the area under the receiver characteristic operating curves (AUC). The identified genetic predictors also differentiate reasonably well between similar hair colors, such as between red and blond-red, as well as between blond and dark-blond, highlighting the value of the identified DNA variants for accurate hair color prediction.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 284 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
Switzerland 2 <1%
Uruguay 1 <1%
Brazil 1 <1%
Italy 1 <1%
Thailand 1 <1%
China 1 <1%
Greece 1 <1%
Spain 1 <1%
Other 0 0%
Unknown 272 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 49 17%
Researcher 46 16%
Student > Bachelor 43 15%
Student > Master 36 13%
Other 15 5%
Other 48 17%
Unknown 47 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 97 34%
Biochemistry, Genetics and Molecular Biology 78 27%
Medicine and Dentistry 22 8%
Chemistry 6 2%
Arts and Humanities 6 2%
Other 18 6%
Unknown 57 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 54. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 June 2023.
All research outputs
#749,909
of 24,626,543 outputs
Outputs from Human Genetics
#46
of 3,060 outputs
Outputs of similar age
#3,341
of 190,252 outputs
Outputs of similar age from Human Genetics
#2
of 13 outputs
Altmetric has tracked 24,626,543 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,060 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.6. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 190,252 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.